Rapido Ride Services with Power BI

Resources: https://www.kaggle.com/datasets/vishaldeoprasad/bangalore-rapido-ride-services-dataset

Overview

This dataset contains comprehensive records of ride services offered by Rapido in Bangalore over a two-month period. It includes various service types such as bike, bike lite, cab economy, auto, and parcel services. Each record provides extensive details about the ride, including the journey from a source to a destination, duration, distance, charges, and payment methods. This dataset is ideal for analyzing transportation patterns, service utilization, and ride economics in Bangalore.

Dataset Structure

The dataset is organized in a tabular format with 50,000 rows and 13 columns. Each row represents a unique ride, and the columns provide detailed information about each ride.

This Project I totally done analysis with Power BI dashboard:

Link: https://app.powerbi.com/view?r=eyJrIjoiMjQ4ZjdmOTctMmJjYi00M2YwLWEzNTQtNjRjOTM2MTU3OTU2IiwidCI6IjhjYWI5ZTZmLTRlMTQtNDcyYS1hNmVjLTFiZDYyOGE0YjZhNCJ9

I this Dashboard it consists of 6 pages:

1. Home

2. Summary

3. Revenue Breakdown

4. Geographical Insights

5. Payment Method & Cancellation Analysis

6. Distance and Duration Analysis

1. Home: Shows the Navigation for each analysis

2. Summary: has the overall view of the Project where it contains cards, donut chart, Line chart, stacked bar chart.

Cards:

Total rides: Displays what are the total rides have been done in the given data set i.e (17-6-2024 to 16-08-2024) approx. 2 months where shows as 50k in card means 50,000 rides have been done.

Adding measures:

Successful=CALCULATE(COUNT(rides_data[ride_id]),rides_data[ride_status]="completed")

Unsuccessful=CALCULATE(COUNT(rides_data[ride_id]),rides_data[ride_status]="cancelled")

Total rides = COUNT(rides_data[ride_id])

Completed rides: Displays what are rides which have completed out of total no of rides was displayed i.e 45k hence 45,000 rides were successfully completed.

Cancelled rides: Displays what are rides which have been cancelled out of total no of rides was displayed i.e 5k hence 5,000 rides were cancelled.

Average fare: Displays What was the Average amount was charge by to the customer for the completion of the ride was “Rs 469.8”

Average ride distance: Displays What was the Average distance travelled by to customer was “Rs 25.5”

Donut chart: It was between Completed rides vs cancelled rides

Line chart:

Average fare price per service: What was the average price for each service i.e bike, car economy, auto, parcel, bike lite?

Stacked bar chart:

Revenue by Service type: What was the Revenue generate for each service?

Number of rides for each service: How many rides have been completed by each service type?

3. Revenue Breakdown:It has revenue insights of the project where its Area chart, Stacked bar chart.

Area Chart:

Daily & Month Revenue Trends: What was the total revenue collected for each day and months irrespective of the service?

Stacked bar chart:

Ride charges vs Misc charges: Has mentioned in dataset description Ride charges was the base fare was the completed ride and Misc charges was the additional charges on the completed rides so this graph showing ride charges vs misc charges.

4. Geographical Insights:In this section I used Cards, Stacked bar chart.

Cards:

Top Source Location: This displays the most book source location out of all bookings.

Learnings: Here have learnt how to use filter in an cards i.e after adding the source column on to the fields column then we to again add source > filter type>drop down select Top n> as we need top 1 so need to add 1 beside shows items top> add Common source measure> click Apply filter.

Common Source= COUNTA(rides_data[source])

Top Destination Location: This displays the most book destination location out of all bookings.

Learnings: Here have learnt how to use filter in an cards i.e after adding the destination column on to the fields column then we to again add destination > filter type>drop down select Top n> as we need top 1 so need to add 1 beside shows items top> add Common destination measure> click Apply filter.

Common destination= COUNTA(rides_data[destination])

Stacked bar chart:

Ride sources with counts: How many times a source was booked/choose by the customer?

Ride destination with counts: How many times a destination was booked/choose by the customer?

5. Payment Method & Cancellation Analysis:In this section I used Table, donut chart, pie chart, Stacked bar chart.

Table:

What are different payment modes used?

How many rides spent on each payment method?

What was the total revenue optioned on each payment mode?

Donut chart:

Payment method distribution: Donut chart Payment mode vs count of ride_id

Stacked bar chart:

Cancelled vs. Completed rides by service type: Service type vs successful & unsuccessful in an single bar chart

Pie chart:

Cancelled % by services: Pie chart with Payment mode vs Cancellation percentage

cancellation percentage =DIVIDE(count(rides_data[services]),CALCULATE(COUNT(rides_data[services]),ALLSELECTED()))

6. Distance and Duration Analysis:In this section I used Cards, Stacked bar chart.

Cards:

Average duration: Displays What was the Average duration travelled by to customer? “64.3”

Min duration: Displays What was the Minimum duration travelled by to customer? “10”

Max duration: Displays What was the Maximum duration travelled by to customer? “119”

Average distance: Displays What was the Average distance travelled by to customer? “25.5”

Max distance: Displays What was the Maximum distance travelled by to customer? “50”

Distance vs Duration: How much time taken for certain distance?

Peak Time: What are the peak hours can be considering the time period?

Which days of week most booking have been book?

I have created new table call date which have date, dayofweek, DayName , Week day or ends columns I have created

Date = ADDCOLUMNS (CALENDAR (DATE (2024, 1, 1), DATE (2024, 12, 31)),"DayOfWeek" , WEEKDAY([Date]))

DayName = FORMAT('Date'[Date],"dddd")

So graph Count of ride_id vs Week day or ends

In conclusion, the analysis of the Rapido ride services dataset using Power BI provides valuable insights into transportation patterns and service utilization in Bangalore. The dashboard effectively visualizes key metrics such as total rides, revenue breakdown, geographical insights, payment methods, and ride cancellations. By examining these aspects, stakeholders can better understand customer preferences, peak usage times, and the financial performance of different service types. This information can be instrumental in making data-driven decisions to enhance service offerings, optimize operations, and improve customer satisfaction. Overall, the project demonstrates the power of data analytics in transforming raw data into actionable insights for the transportation industry.